Bringing a new drug to market is a costly and time-consuming process, but artificial intelligence is transforming the landscape. By accelerating patient recruitment, streamlining site selection and enhancing data analysis, AI is eliminating bottlenecks and making drug development more efficient and cost-effective. The result is faster access to life-saving treatments and significant cost reductions for pharmaceutical companies.
The high cost and prolonged timelines of clinical trials have long been major hurdles in drug development. According to research from the Tufts Center for the Study of Drug Development, the average cost of bringing a new drug to market is approximately $2.6 billion, with clinical trials consuming a substantial portion of that expense. A significant challenge is patient recruitment, where finding suitable participants for a study often delays progress. Manual data collection and analysis further compound these inefficiencies.
Artificial intelligence is addressing these challenges by leveraging historical data to optimise site selection and patient matching. By analysing vast datasets, AI can pinpoint the most appropriate clinical sites and identify patients who meet precise criteria. This reduces recruitment times and minimises trial costs, allowing pharmaceutical companies to streamline operations. One standout example is Exscientia, which used AI to design a cancer immunotherapy molecule in under 12 months—a process that traditionally takes four to five years.
Beyond clinical trials, AI is revolutionising the broader drug development process. Advanced machine learning models can refine trial protocols, optimise execution and facilitate biomarker discovery, which is essential for targeted therapies. By processing large datasets, AI detects patterns and insights that human researchers might overlook. The result is more efficient patient stratification and the ability to develop precision treatments tailored to specific genetic profiles. Traditional in silico models often require five to six years to complete, whereas AI-driven optimisations can reduce this to just a few months, significantly lowering costs.
Despite its advantages, AI-driven clinical trials must address regulatory and ethical concerns. Data integrity and validation are critical, as regulatory agencies such as the FDA work to establish frameworks for AI-based approvals. Ensuring compliance with global regulations, including HIPAA, ISO 13485 and GDPR, is essential for maintaining security and patient privacy. AI also has the potential to improve diversity in clinical trials by eliminating biases in patient selection. By objectively analysing data, AI can recruit patients from diverse backgrounds more effectively than traditional methods.
The adoption of AI in clinical trials is accelerating, with projections indicating that within five years, nearly 80-90% of trials will incorporate AI-driven processes. Emerging technologies, such as AI-based genomic signature prediction tools, are enhancing precision medicine by enabling hospitals to analyse high-resolution images and identify patients who may benefit from specific treatments. This innovation is expanding access to personalised therapies and improving patient outcomes.
For AI to fully realise its potential, collaboration between pharmaceutical companies, AI innovators and regulatory bodies must continue to evolve. Regulatory agencies must establish clear guidelines for validating AI-driven data to ensure consistency with existing standards. As frameworks develop and industry partnerships grow, AI-driven advancements will continue to shape the future of clinical trials and precision medicine. By reducing costs, accelerating timelines and improving patient recruitment, AI is set to redefine drug development, ultimately making life-saving treatments more accessible to patients worldwide.
Poolbeg Pharma plc (LON:POLB) is a clinical stage infectious disease pharmaceutical company, with a novel capital light clinical model which enables us to develop multiple products faster and more cost effectively than the traditional biotech model.
